Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities

Development of Deep Learning Methods for Magnetic Resonance Phase Imaging of Neurological Disease

Magnetic resonance imaging (MRI) is a high-resolution, non-invasive medical imaging modality that is widely used in human brain. In recent years, susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) have been proposed to utilize MR phase signal to generate contrast from tissue magnetic susceptibility and even quantify the property. On the other hand, deep learning, especially deep convolutional neural networks (DCNNs), have achieved state-of-the-art performances in numerous computer vision tasks and gained significant attention in the field of medical imaging in the recent years. This dissertation combined the idea of deep learning with the two MR phase imaging methods. To combined deep learning with SWI, we designed and trained a 3D deep residual network that can distinguish false positive detected candidates from cerebral microbleeds (CMBs) and built an automatic CMB detection pipeline with high performance. We further confirmed the generalizability of this deep learning-based pipeline using multiple dataset with different scan parameters and pathologies and provided lessons for application and generalization of generic deep learning based medical imaging methods.To combine deep learning with QSM, we developed a 3D U-Net based network that learns to perform dipole inversion from gold standard QSM acquired from data with multiple orientation. The model was further improved with adversarial training strategy and achieved significantly lower reconstruction error than traditional QSM algorithms. In addition, we also performed various background removal and dipole inversion algorithms on both brain tumor patients and healthy volunteers to study and compare their performances. The results could provide guidance on future application of QSM in different scenarios

DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies

IDENTIFICATION OF OCCULT CEREBRAL MICROBLEEDS IN ADULTS WITH IMMUNE THROMBOCYTOPENIA

Management of symptoms and prevention of life-threatening hemorrhage in immune thrombocytopenia (ITP) must be balanced against adverse effects of therapies. Because current treatment guidelines based on platelet count are confounded by variable bleeding phenotypes, there is a need to identify new objective markers of disease severity for treatment stratification. In this cross-sectional prospective study of 49 patients with ITP and nadir platelet counts <30 × 109/L and 18 aged-matched healthy controls, we used susceptibility-weighted magnetic resonance imaging to detect cerebral microbleeds (CMBs) as a marker of occult hemorrhage. CMBs were detected using a semiautomated method and correlated with clinical metadata using multivariate regression analysis. No CMBs were detected in health controls. In contrast, lobar CMBs were identified in 43% (21 of 49) of patients with ITP; prevalence increased with decreasing nadir platelet count (0/4, ≥15 × 109/L; 2/9, 10-14 × 109/L; 4/11, 5-9 × 109/L; 15/25 <5 × 109/L) and was associated with longer disease duration (P = 7 × 10−6), lower nadir platelet count (P = .005), lower platelet count at time of neuroimaging (P = .029), and higher organ bleeding scores (P = .028). Mucosal and skin bleeding scores, number of previous treatments, age, and sex were not associated with CMBs. Occult cerebral microhemorrhage is common in patients with moderate to severe ITP. Strong associations with ITP duration may reflect CMB accrual over time or more refractory disease. Further longitudinal studies in children and adults will allow greater understanding of the natural history and clinical and prognostic significance of CMBs

Assessment of Post-Treatment Imaging Changes Following Radiotherapy using Magnetic Susceptibility Techniques

Radiation therapy (RT) is a common treatment for brain neoplasms and is used alone or in combination with other therapies. The use of RT has been found to be successful in controlling tumors and extending the overall survival of patients; however, there are many unanswered questions regarding radiotherapy effects in the normal brain surrounding or infiltrated by tumor. Changes to the vascular and parenchyma have been documented, and more recently inflammatory mechanisms have been postulated to play a role in radiation injury. Traditional imaging techniques used within the clinic (CT and MRI) are often lacking in their ability to differentiate between recurrent tumor, transient treatment effects, or radiation necrosis. The primary goal of this thesis is to demonstrate an MRI acquisition method that has been shown to be sensitive to deoxygenated blood and iron content as a potential biomarker of radiation effect on the normal brain. Specifically, post-processing techniques are used to determine the applicability of qualitative images such as Susceptibility-Weighted Imaging (SWI) and quantitative methods such as Quantitative Susceptibility Mapping (QSM) and apparent traverse relaxation (R2*) using the same sequence. These methods are potential surrogate markers for vascular changes and neuroinflammatory components that could predict sub-acute and long-term radiation effects. Within this thesis, R2* is shown to be a promising marker for the prediction of radiation necrosis, whereas SWI and QSM are shown to be excellent modalities for detecting longterm effects such as microbleeds. Additionally, R2 * is shown to be a potentially useful technique in identifying post-imaging treatment changes (pseudoprogression) following chemoradiotherapy for malignant glioma. Finally, the use of this non-contrast method shows promise for integration within a clinical setting and the potential for expansion to multicenter clinical trials

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the \textit{VAscular Lesions DetectiOn and Segmentation} (\textit{Where is VALDO?}) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1 - EPVS, 9 for Task 2 - Microbleeds and 6 for Task 3 - Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1 - EPVS and Task 2 - Microbleeds and not practically useful results yet for Task 3 - Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level

Accelerated development of cerebral small vessel disease in young stroke patients.

OBJECTIVE: To study the long-term prevalence of small vessel disease after young stroke and to compare this to healthy controls. METHODS: This prospective cohort study comprises 337 patients with an ischemic stroke or TIA, aged 18-50 years, without a history of TIA or stroke. In addition, 90 age- and sex-matched controls were included. At follow-up, lacunes, microbleeds, and white matter hyperintensity (WMH) volume were assessed using MRI. To investigate the relation between risk factors and small vessel disease, logistic and linear regression were used. RESULTS: After mean follow-up of 9.9 (SD 8.1) years, 337 patients were included (227 with an ischemic stroke and 110 with a TIA). Mean age of patients was 49.8 years (SD 10.3) and 45.4% were men; for controls, mean age was 49.4 years (SD 11.9) and 45.6% were men. Compared with controls, patients more often had at least 1 lacune (24.0% vs 4.5%, p < 0.0001). In addition, they had a higher WMH volume (median 1.5 mL [interquartile range (IQR) 0.5-3.7] vs 0.4 mL [IQR 0.0-1.0], p < 0.001). Compared with controls, patients had the same volume WMHs on average 10-20 years earlier. In the patient group, age at stroke (β = 0.03, 95% confidence interval [CI] 0.02-0.04) hypertension (β = 0.22, 95% CI 0.04-0.39), and smoking (β = 0.18, 95% CI 0.01-0.34) at baseline were associated with WMH volume. CONCLUSIONS: Patients with a young stroke have a higher burden of small vessel disease than controls adjusted for confounders. Cerebral aging seems accelerated by 10-20 years in these patients, which may suggest an increased vulnerability to vascular risk factors.This is the final version of the article. It first appeared from Wolters Kluwer via https://doi.org/10.​1212/​WNL.​0000000000003123

Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results

Objectives: To diagnose Parkinson disease (PD) at the individual level using pattern recognition of brain susceptibility-weighted imaging (SWI). Methods: We analysed brain SWI in 36 consecutive patients with Parkinsonism suggestive of PD who had (1) SWI at 3T, (2) brain 123I-ioflupane SPECT and (3) extensive neurological testing including follow-up (16 PD, 67.4 ± 6.2years, 11 female; 20 OTHER, a heterogeneous group of atypical Parkinsonism syndromes 65.2 ± 12.5years, 6 female). Analysis included group-level comparison of SWI values and individual-level support vector machine (SVM) analysis. Results: At the group level, simple visual analysis yielded no differences between groups. However, the group-level analyses demonstrated increased SWI in the bilateral thalamus and left substantia nigra in PD patients versus other Parkinsonism. The inverse comparison yielded no supra-threshold clusters. At the individual level, SVM correctly classified PD patients with an accuracy above 86%. Conclusions: SVM pattern recognition of SWI data provides accurate discrimination of PD among patients with various forms of Parkinsonism at an individual level, despite the absence of visually detectable alterations. This pilot study warrants further confirmation in a larger cohort of PD patients and with different MR machines and MR parameters. Key Points: • Magnetic resonance imaging data offers new insights into Parkinson's disease • Visual susceptibility-weighted imaging (SWI) analysis could not discriminate idiopathic from atypical PD • However, support vector machine (SVM) analysis provided highly accurate detection of idiopathic PD • SVM analysis may contribute to the clinical diagnosis of individual PD patients • Such information can be readily obtained from routine MR dat